EP3455759A4 - Neural network architectures for scoring and visualizing biological sequence variations using molecular phenotype, and systems and methods therefor - Google Patents

Neural network architectures for scoring and visualizing biological sequence variations using molecular phenotype, and systems and methods therefor Download PDF

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Publication number
EP3455759A4
EP3455759A4 EP16901179.8A EP16901179A EP3455759A4 EP 3455759 A4 EP3455759 A4 EP 3455759A4 EP 16901179 A EP16901179 A EP 16901179A EP 3455759 A4 EP3455759 A4 EP 3455759A4
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EP
European Patent Office
Prior art keywords
scoring
systems
neural network
network architectures
sequence variations
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP16901179.8A
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German (de)
French (fr)
Other versions
EP3455759A1 (en
Inventor
Andrew Delong
Brendan Frey
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Deep Genomics Inc
Original Assignee
Deep Genomics Inc
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Filing date
Publication date
Application filed by Deep Genomics Inc filed Critical Deep Genomics Inc
Publication of EP3455759A1 publication Critical patent/EP3455759A1/en
Publication of EP3455759A4 publication Critical patent/EP3455759A4/en
Pending legal-status Critical Current

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B45/00—ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/002—Biomolecular computers, i.e. using biomolecules, proteins, cells
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • G06N5/046—Forward inferencing; Production systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B99/00—Subject matter not provided for in other groups of this subclass
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6813—Hybridisation assays
    • C12Q1/6827—Hybridisation assays for detection of mutation or polymorphism
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Data Mining & Analysis (AREA)
  • General Health & Medical Sciences (AREA)
  • Biophysics (AREA)
  • Medical Informatics (AREA)
  • Evolutionary Computation (AREA)
  • Software Systems (AREA)
  • Artificial Intelligence (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Evolutionary Biology (AREA)
  • Biotechnology (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Computational Linguistics (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Molecular Biology (AREA)
  • Biomedical Technology (AREA)
  • Bioethics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Databases & Information Systems (AREA)
  • Epidemiology (AREA)
  • Public Health (AREA)
  • Chemical & Material Sciences (AREA)
  • Organic Chemistry (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
EP16901179.8A 2016-05-13 2016-07-04 Neural network architectures for scoring and visualizing biological sequence variations using molecular phenotype, and systems and methods therefor Pending EP3455759A4 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US201662335810P 2016-05-13 2016-05-13
PCT/CA2016/050776 WO2017193198A1 (en) 2016-05-13 2016-07-04 Neural network architectures for scoring and visualizing biological sequence variations using molecular phenotype, and systems and methods therefor

Publications (2)

Publication Number Publication Date
EP3455759A1 EP3455759A1 (en) 2019-03-20
EP3455759A4 true EP3455759A4 (en) 2020-01-01

Family

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Family Applications (1)

Application Number Title Priority Date Filing Date
EP16901179.8A Pending EP3455759A4 (en) 2016-05-13 2016-07-04 Neural network architectures for scoring and visualizing biological sequence variations using molecular phenotype, and systems and methods therefor

Country Status (4)

Country Link
US (1) US11568960B2 (en)
EP (1) EP3455759A4 (en)
CA (1) CA3024017C (en)
WO (1) WO2017193198A1 (en)

Families Citing this family (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10185803B2 (en) 2015-06-15 2019-01-22 Deep Genomics Incorporated Systems and methods for classifying, prioritizing and interpreting genetic variants and therapies using a deep neural network
WO2017193198A1 (en) 2016-05-13 2017-11-16 Deep Genomics Incorporated Neural network architectures for scoring and visualizing biological sequence variations using molecular phenotype, and systems and methods therefor
US10599975B2 (en) * 2017-12-15 2020-03-24 Uber Technologies, Inc. Scalable parameter encoding of artificial neural networks obtained via an evolutionary process
WO2019237230A1 (en) * 2018-06-11 2019-12-19 深圳华大生命科学研究院 Method and system for determining type of sample to be tested
US11842794B2 (en) 2019-03-19 2023-12-12 The University Of Hong Kong Variant calling in single molecule sequencing using a convolutional neural network
US20230402127A1 (en) * 2020-08-21 2023-12-14 Inari Agriculture Technology, Inc. Machine learning-based variant effect assessment and uses thereof
CN113362892B (en) * 2021-06-16 2021-12-17 北京阅微基因技术股份有限公司 Method for detecting and typing repetition number of short tandem repeat sequence

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2004061616A2 (en) * 2002-12-27 2004-07-22 Rosetta Inpharmatics Llc Computer systems and methods for associating genes with traits using cross species data
US9890429B2 (en) * 2012-02-29 2018-02-13 Dana-Farber Cancer Institute, Inc. Compositions, kits, and methods for the identification, assessment, prevention, and therapy of cancer
US9418203B2 (en) * 2013-03-15 2016-08-16 Cypher Genomics, Inc. Systems and methods for genomic variant annotation
US10185803B2 (en) * 2015-06-15 2019-01-22 Deep Genomics Incorporated Systems and methods for classifying, prioritizing and interpreting genetic variants and therapies using a deep neural network
WO2017193198A1 (en) 2016-05-13 2017-11-16 Deep Genomics Incorporated Neural network architectures for scoring and visualizing biological sequence variations using molecular phenotype, and systems and methods therefor
US20180107927A1 (en) * 2016-06-15 2018-04-19 Deep Genomics Incorporated Architectures for training neural networks using biological sequences, conservation, and molecular phenotypes
EP3479304A4 (en) * 2016-07-04 2020-04-08 Deep Genomics Incorporated Systems and methods for generating and training convolutional neural networks using biological sequences and relevance scores derived from structural, biochemical, population and evolutionary data

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
"Nanotechnology: Science and Computation", 31 January 2009, SPRINGER BERLIN HEIDELBERG, DE, ISSN: 1619-7127, article DANIEL S. YEUNG ET AL: "Principles of Sensitivity Analysis", pages: 17 - 24, XP055639689, DOI: 10.1007/978-3-642-02532-7_2 *
I CIUCAI ET AL: "On the Irrelevant Feature Discarding in Neural Networks and its Applications in Medicine", STUDIES IN HEALTH TECHNOLOGY AND INFORMATICS, 31 January 1996 (1996-01-31), Amsterdam, IOS Press, Tokyo, XP055639697, ISSN: 0926-9630, ISBN: 9051992785, Retrieved from the Internet <URL:http://ebooks.iospress.nl/volumearticle/16229> [retrieved on 20191106] *
See also references of WO2017193198A1 *
SHAOMIN YAN ET AL: "Prediction of Mutation Positions in H5N1 Neuraminidases From Influenza A Virus by Means of Neural Network", ANNALS OF BIOMEDICAL ENGINEERING, KLUWER ACADEMIC PUBLISHERS-PLENUM PUBLISHERS, NE, vol. 38, no. 3, 13 January 2010 (2010-01-13), pages 984 - 992, XP019786040, ISSN: 1573-9686 *

Also Published As

Publication number Publication date
US11568960B2 (en) 2023-01-31
US20190138878A1 (en) 2019-05-09
CA3024017C (en) 2024-03-19
WO2017193198A1 (en) 2017-11-16
CA3024017A1 (en) 2017-11-16
EP3455759A1 (en) 2019-03-20

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